Machine Learning for Data Science and Analytics - Columbia UniversityedX
What you'll learn on the course
Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis. This is the second course in the three-part Data Science and Analytics XSeries.
What you'll learn
What you'll learn
- What machine learning is and how it is related to statistics and data analysis
- How machine learning uses computer algorithms to search for patterns in data
- How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
- How to uncover hidden themes in large collections of documents using topic modeling
- How to prepare data, deal with missing data and create custom data analysis solutions for different industries
- Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming
Ansaf Salleb-Aouissi Ansaf Salleb-Aouissi joined the Department of Computer Science as a Lecturer in Discipline in July 2015. Ansaf received her PhD in Computer Science from University of Orleans, France in 2003, after which she pursued her training as a postdoctoral fellow at INRIA, Rennes (France). She was appointed as an Associate Research Scientist at the Columbia University’s Center for Computational Learning Systems in 2006 and served as an adjunct professor with the Computer Science department and the Data Science Institute in 2014 and 2015.